Determination methods for identifying types of defects are installed as the core part of software in Ultra-High Frequency (UHF) partial discharge diagnosis online systems and portable equipment which are currently used for Gas Insulated Switchgear (GIS) defect prevention.
This determination method for defect identification indicates determination results in the form of the probability of matching predefined defect types using a neural network, and such a neural network determines the type of defect in a detected partial discharge signal using several hundreds or more pieces of training data obtained based on phase, discharge quantity, the number of discharges, etc.
A neural network determination method that is currently used has fundamental problems in that the precision of defect type determination is greatly deteriorated in parameters based on untrained patterns, and the type of defect is falsely determined to be an incorrect defect type, and thus determination for all potential defects must be simulated and trained.
In particular, in online systems, a large number of events occur, and a lot of manpower and time are required to individually check and analyze such events. In many cases, even portable equipment indicates incorrect defect types as results. In this case, similar to online systems, a lot of manpower and time are required to determine the states of devices, and there is a possibility to falsely cope with defects, so that, if a defect is determined to be noise, the occurrence of failures cannot be previously prevented, whereas if noise is determined to be a defect, economic loss attributable to excessive inspections may be caused.